Skip to main content
SpringerLink
Log in

Formulation of Error Generation-Based SRGMs Under the Influence of Irregular Fluctuations

  • Chapter
  • First Online:
System Performance and Management Analytics

Part of the book series: Asset Analytics ((ASAN))

Abstract

Reliability growth models for software have been widely studied in the literature. Many schemes (like hazard rate function, queuing theory, and random lag function) have been proposed and utilized for modeling the fault removal phenomenon. Among these, hazard rate function technique has gained significant attention and has been excessively used for model debugging process. An essential aspect of modeling has been pertaining to reliability estimation under irregular fluctuations environment. Another major domain highlighted in Software Reliability Engineering (SRE) is that of error generation, which has been an important area of research up till now. This article shows how, using Hazard Rate Function approach, error generation concept can be studied in a fluctuating environment. The utility of the proposed framework has been emphasized in this paper through some models pertaining to different conditions. The applicability of our proposed models and comparisons in terms of goodness-of-fit and predictive validity has been presented using known software failure data sets.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Goel, A. L., & Okumoto, K. (1979). Time-dependent error-detection rate model for software reliability and other performance measures. IEEE Transactions on Reliability, 28(3), 206–211.

    Article  Google Scholar 

  2. Kapur, P. K., Garmabaki, A. S., & Singh, J. (2011). Multi up-gradation software reliability model with imperfect debugging. In Proceedings of the International Congress on Productivity, Quality, Reliability, Optimization and Modeling (ICPQROM); Feb 7–8; New Delhi, India, 136.

    Google Scholar 

  3. Kapur, P. K., Pham, H., Gupta, A., & Jha, P. C. (2011). Software reliability assessment with OR applications. London: Springer.

    Book  Google Scholar 

  4. Kapur, P. K., Tandon, A., & Kaur, G. (2010, December). Multi up-gradation software reliability model. In Reliability, Safety and Hazard (ICRESH), 2010 2nd International Conference on (pp. 468–474). IEEE.

    Google Scholar 

  5. Musa, J. D., Iannino, A., & Okumoto, K. (1987). Software reliability: measurement, prediction, application. McGraw-Hill, Inc.

    Google Scholar 

  6. Pham, H. (2006). Software Reliability Modeling. In System Software Reliability (pp. 153–177). London: Springer.

    Google Scholar 

  7. Yamada, S., Ohba, M., & Osaki, S. (1984). S-shaped software reliability growth models and their applications. IEEE Transactions on Reliability, 33(4), 289–292.

    Article  Google Scholar 

  8. Anand, A., Deepika, Singh, N. & Dutt, P. (2016). Software reliability growth modeling based on in house testing and field testing. Communication in dependability and quality management: An International Journal, 19(1), 74–84.

    Google Scholar 

  9. Hudson, G. R. (1967). Program errors as a birth and death process. System Development Corporation. Report SP-3011, Santa Monica, CA.

    Google Scholar 

  10. Jelinski, Z., & Moranda, P. B. (1972). Software reliability research, Statistical Computer Performance Evaluation. In W. Freiberger (Ed.), 465–484.

    Google Scholar 

  11. Moranda, P. B. (1975, January). Prediction of software reliability during debugging. In Proceedings Annual Reliability and Maintainability Symposium (No. Jan 28, pp. 327–332). 345 E 47th St, New York, NY 10017-2394: IEEE-Inst Electrical Electronics Engineers Inc.

    Google Scholar 

  12. Schneidewind, N. F. (1972, December). An approach to software reliability prediction and quality control. In Proceedings of the December 5–7, 1972, fall joint computer conference, part II (pp. 837-847). ACM.

    Google Scholar 

  13. Musa, J. D. (1975). A theory of software reliability and its application. IEEE Transactions on Software Engineering, 3, 312–327.

    Article  Google Scholar 

  14. Anand, A., Das, S., & Singh, O. (2016, September). Modeling software failures and reliability growth based on pre & post release testing. In Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO), 2016 5th International Conference on (pp. 139–144). IEEE.

    Google Scholar 

  15. Anand, A., Deepika, & Singh, O. (2016). Incorporating features enhancement archetype in software reliability growth modeling and optimal release time determination. International Journal of Computer Applications (0975 – 8887), 139(4) 1–6.

    Google Scholar 

  16. Yamada, S., Nishigaki, A., & Kimura, M. (2003). A stochastic differential equation model for software reliability assessment and its goodness of fit. International Journal of Reliability and Applications, 4(1), 1–11.

    Google Scholar 

  17. Yamada, S., Ohba, M., & Osaki, S. (1983). S-shaped reliability growth modeling for software error detection. IEEE Transactions on Reliability, 32(5), 475–484.

    Article  Google Scholar 

  18. Shyur, H. J. (2003). A stochastic software reliability model with imperfect-debugging and change-point. Journal of Systems and Software, 66(2), 135–141.

    Article  Google Scholar 

  19. Kapur, P. K., Anand, S., Yamada, S., & Yadavalli, V. S. (2009). Stochastic differential equation-based flexible software reliability growth model. Mathematical Problems in Engineering.

    Google Scholar 

  20. Kapur, P. K., Anand, S., Yadav, K., & Singh, J. (2012). A unified scheme for developing software reliability growth models using stochastic differential equations. International Journal of Operational Research, 15(1), 48–63.

    Article  Google Scholar 

  21. Singh, O., Kapur, P. K., & Anand, A. (2011, December). A stochastic formulation of successive software releases with faults severity. In Industrial Engineering and Engineering Management (IEEM), 2011 IEEE International Conference on (pp. 136–140). IEEE.

    Google Scholar 

  22. Tamura, Y., & Yamada, S. (2006). A flexible stochastic differential equation model in distributed development environment. European Journal of Operational Research, 168(1), 143–152.

    Article  Google Scholar 

  23. Lee, C. H., Kim, Y. T., & Park, D. H. (2004). S-shaped software reliability growth models derived from stochastic differential equations. IIE Transactions, 36(12), 1193–1199.

    Article  Google Scholar 

  24. Øksendal, B. (2003). Stochastic differential equations. In Stochastic differential equations (pp. 65–84). Berlin Heidelberg: Springer.

    Google Scholar 

  25. Singh, O., Kapur, P. K., Anand, A., & Singh, J. (2009). Stochastic Differential Equation based Modeling for Multiple Generations of Software. In Proceedings of Fourth International Conference on Quality, Reliability and Infocom Technology (ICQRIT), Trends and Future Directions, Narosa Publications (pp. 122–131).

    Google Scholar 

  26. Singh, O., Anand, A., Kapur, P. K., & Aggrawal, D. (2012). Consumer behaviour-based innovation diffusion modelling using stochastic differential equation incorporating change in adoption rate. International Journal of Technology Marketing, 7(4), 346–360.

    Article  Google Scholar 

  27. Gardiner, C. (1985). Handbook of stochastic methods (Vol. 4). Berlin: Springer.

    Google Scholar 

  28. Anand, A., Kapur, P. K., Agarwal, M., & Aggrawal, D. (2014, October). Generalized innovation diffusion modeling & weighted criteria based ranking. In Reliability, Infocom Technologies and Optimization (ICRITO)(Trends and Future Directions), 2014 3rd International Conference on (pp. 1–6). IEEE.

    Google Scholar 

  29. Deepika, Singh, O., Anand, A., & Singh J. N. P. (2017). Testing domain dependent software reliability growth models. International Journal of Mathematical, Engineering and Management sciences. 2(3), 140–149.

    Google Scholar 

  30. SAS, S. (2004). STAT user guide, version 9.1. 2. Cary, NC, USA: SAS Institute Inc,.

    Google Scholar 

  31. Wood, A. (1996). Predicting software reliability. Computer, 29(11), 69–77.

    Article  Google Scholar 

  32. Kanoun, K., de Bastos Martini, M. R., & De Souza, J. M. (1991). A method for software reliability analysis and prediction application to the TROPICO-R switching system. IEEE Transactions on Software Engineering, 17(4), 334–344.

    Article  Google Scholar 

Download references

Acknowledgements

The research work presented in this chapter has been supported by grants to first and third author from Department of Science and Technology, via DST PURSE phase II, India.

Author information

Authors and Affiliations

  1. Department of Operational Research , University of Delhi, Delhi, New Delhi, 110007, India

    Adarsh Anand,  Deepika & Ompal Singh

Authors
  1. Adarsh Anand
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Deepika
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ompal Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Deepika .

Editor information

Editors and Affiliations

  1. Center for Interdisciplinary Research, Amity University, New Delhi, India

    P. K. Kapur

  2. Department of Economics and Management in Machine Building, Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russia

    Yury Klochkov

  3. Western Norway University of Applied Sciences, Bergen, Norway

    Ajit Kumar Verma

  4. Amity University, Noida, Uttar Pradesh, India

    Gurinder Singh

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Anand, A., Deepika, Singh, O. (2019). Formulation of Error Generation-Based SRGMs Under the Influence of Irregular Fluctuations. In: Kapur, P., Klochkov, Y., Verma, A., Singh, G. (eds) System Performance and Management Analytics. Asset Analytics. Springer, Singapore. https://doi.org/10.1007/978-981-10-7323-6_10

Download citation

Publish with us

Policies and ethics

Search

Navigation